Computational efficiency is defined as a computational rate per available power. Classic design approaches do not always achieve optimal efficiency due to thermal losses of voltage regulation and non-optimal load-matching for variable power sources. Furthermore, the classic design approaches may include components, e.g., voltage regulators, that have a high cost.
In low-power environments, e.g., a solar power environment, not only may the power supply be low, but the power supply may vary as the environmental conditions generating the power vary. For example, the sun may move behind clouds, thus reducing the amount of power generated by a solar cell. Under these conditions, classic design approaches utilize voltage regulation to output a constant voltage, to minimize the variable power of the power source. However, voltage regulation requires the use of voltage regulators, which have a high associated cost. Additionally, voltage regulation has an associated thermal power loss. Power losses, e.g., thermal losses, in low-power environments may be intolerable, as in low-power environments, by definition, there is not much excess power available.
Additionally, classic design approaches do not always achieve optimal efficiency due to non-optimal load-matching for variable power sources. As low-power environments may produce varying amounts of power under different operating conditions, or environmental conditions, non-optimal load-matching may occur. For example, on a day with scattered clouds, a solar cell may produce varying amounts of power as the clouds move in and out between the sun and the solar cell. Classic design approaches may not prevent non-optimal load matching that may occur under these dynamic conditions
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.